Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations14787
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory200.0 B

Variable types

Text8
DateTime1
Categorical4
Numeric12

Alerts

actual_distance_to_destination is highly overall correlated with actual_time and 8 other fieldsHigh correlation
actual_time is highly overall correlated with actual_distance_to_destination and 7 other fieldsHigh correlation
dest_state is highly overall correlated with source_stateHigh correlation
od_time_diff_hour is highly overall correlated with actual_distance_to_destination and 8 other fieldsHigh correlation
osrm_distance is highly overall correlated with actual_distance_to_destination and 8 other fieldsHigh correlation
osrm_time is highly overall correlated with actual_distance_to_destination and 8 other fieldsHigh correlation
route_type is highly overall correlated with actual_distance_to_destination and 5 other fieldsHigh correlation
segment_actual_time_sum is highly overall correlated with actual_distance_to_destination and 7 other fieldsHigh correlation
segment_osrm_distance_sum is highly overall correlated with actual_distance_to_destination and 7 other fieldsHigh correlation
segment_osrm_time_sum is highly overall correlated with actual_distance_to_destination and 8 other fieldsHigh correlation
source_state is highly overall correlated with dest_stateHigh correlation
start_scan_to_end_scan is highly overall correlated with actual_distance_to_destination and 8 other fieldsHigh correlation
trip_day is highly overall correlated with trip_monthHigh correlation
trip_month is highly overall correlated with trip_dayHigh correlation
trip_uuid has unique values Unique
trip_creation_time has unique values Unique
od_time_diff_hour has unique values Unique
trip_hour has 991 (6.7%) zeros Zeros
trip_dayofweek has 1980 (13.4%) zeros Zeros

Reproduction

Analysis started2025-04-24 17:19:51.037758
Analysis finished2025-04-24 17:20:16.423801
Duration25.39 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

trip_uuid
Text

Unique 

Distinct14787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:16.635933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length23
Mean length23.000744
Min length23

Characters and Unicode

Total characters340112
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14787 ?
Unique (%)100.0%

Sample

1st rowtrip-153671041653548748
2nd rowtrip-153671042288605164
3rd rowtrip-153671043369099517
4th rowtrip-153671046011330457
5th rowtrip-153671052974046625
ValueCountFrequency (%)
trip-153671046011330457 1
 
< 0.1%
trip-153861118270144424 1
 
< 0.1%
trip-153671041653548748 1
 
< 0.1%
trip-153861042148375909 1
 
< 0.1%
trip-153861052318770017 1
 
< 0.1%
trip-153861055072891350 1
 
< 0.1%
trip-153861059679001096 1
 
< 0.1%
trip-153861073259859847 1
 
< 0.1%
trip-153861075467184898 1
 
< 0.1%
trip-153861089403973335 1
 
< 0.1%
Other values (14777) 14777
99.9%
2025-04-24T17:20:16.980886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 35458
10.4%
3 35367
10.4%
1 35141
10.3%
7 28883
 
8.5%
8 25138
 
7.4%
6 22675
 
6.7%
0 21072
 
6.2%
9 20942
 
6.2%
2 20831
 
6.1%
4 20670
 
6.1%
Other values (5) 73935
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 340112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 35458
10.4%
3 35367
10.4%
1 35141
10.3%
7 28883
 
8.5%
8 25138
 
7.4%
6 22675
 
6.7%
0 21072
 
6.2%
9 20942
 
6.2%
2 20831
 
6.1%
4 20670
 
6.1%
Other values (5) 73935
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 340112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 35458
10.4%
3 35367
10.4%
1 35141
10.3%
7 28883
 
8.5%
8 25138
 
7.4%
6 22675
 
6.7%
0 21072
 
6.2%
9 20942
 
6.2%
2 20831
 
6.1%
4 20670
 
6.1%
Other values (5) 73935
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 340112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 35458
10.4%
3 35367
10.4%
1 35141
10.3%
7 28883
 
8.5%
8 25138
 
7.4%
6 22675
 
6.7%
0 21072
 
6.2%
9 20942
 
6.2%
2 20831
 
6.1%
4 20670
 
6.1%
Other values (5) 73935
21.7%

trip_creation_time
Date

Unique 

Distinct14787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Minimum2018-09-12 00:00:16.535741
Maximum2018-10-03 23:59:42.701692
Invalid dates0
Invalid dates (%)0.0%
2025-04-24T17:20:17.113717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:17.259627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1497
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:17.596483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length51
Mean length51
Min length51

Characters and Unicode

Total characters754137
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)1.1%

Sample

1st rowthanos::sroute:d7c989ba-a29b-4a0b-b2f4-288cdc60074b
2nd rowthanos::sroute:3a1b0ab2-bb0b-4c53-8c59-eb2a2c0d68b9
3rd rowthanos::sroute:de5e208e-7641-45e6-8100-4d9fb1e5720d
4th rowthanos::sroute:f0176492-a679-4597-8332-bbd1c7f9f442
5th rowthanos::sroute:d9f07b12-65e0-4f3b-bec8-df0613461b0f
ValueCountFrequency (%)
thanos::sroute:a16bfa03-3462-4bce-9c82-5784c7d315e6 53
 
0.4%
thanos::sroute:8e6cd941-adb5-4f66-b44f-84938f5fca03 46
 
0.3%
thanos::sroute:c736b86f-5c1d-4497-832c-876db4c1a518 43
 
0.3%
thanos::sroute:ca336899-47aa-4622-9885-b9fbb2302aeb 41
 
0.3%
thanos::sroute:5f8fb6cf-814a-426f-b2c9-8665b605d343 40
 
0.3%
thanos::sroute:3a78a6e9-ef19-43ae-832e-e4a3868eb32d 39
 
0.3%
thanos::sroute:5dd1132a-b8a0-4b55-9df1-08937607bb65 37
 
0.3%
thanos::sroute:b743d024-a50a-40a4-8ffd-e3b420b7534e 36
 
0.2%
thanos::sroute:ad01057e-be3e-42d5-8380-4fd6d174ec89 35
 
0.2%
thanos::sroute:faef8565-4919-4d38-b5a6-a81f9c1da35c 34
 
0.2%
Other values (1487) 14383
97.3%
2025-04-24T17:20:18.003059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 59148
 
7.8%
a 47233
 
6.3%
: 44361
 
5.9%
e 42479
 
5.6%
4 41899
 
5.6%
9 31478
 
4.2%
8 31234
 
4.1%
b 30824
 
4.1%
t 29574
 
3.9%
o 29574
 
3.9%
Other values (15) 366333
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 754137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 59148
 
7.8%
a 47233
 
6.3%
: 44361
 
5.9%
e 42479
 
5.6%
4 41899
 
5.6%
9 31478
 
4.2%
8 31234
 
4.1%
b 30824
 
4.1%
t 29574
 
3.9%
o 29574
 
3.9%
Other values (15) 366333
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 754137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 59148
 
7.8%
a 47233
 
6.3%
: 44361
 
5.9%
e 42479
 
5.6%
4 41899
 
5.6%
9 31478
 
4.2%
8 31234
 
4.1%
b 30824
 
4.1%
t 29574
 
3.9%
o 29574
 
3.9%
Other values (15) 366333
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 754137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 59148
 
7.8%
a 47233
 
6.3%
: 44361
 
5.9%
e 42479
 
5.6%
4 41899
 
5.6%
9 31478
 
4.2%
8 31234
 
4.1%
b 30824
 
4.1%
t 29574
 
3.9%
o 29574
 
3.9%
Other values (15) 366333
48.6%

route_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
Carting
8906 
FTL
5881 

Length

Max length7
Median length7
Mean length5.4091432
Min length3

Characters and Unicode

Total characters79985
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFTL
2nd rowCarting
3rd rowFTL
4th rowCarting
5th rowFTL

Common Values

ValueCountFrequency (%)
Carting 8906
60.2%
FTL 5881
39.8%

Length

2025-04-24T17:20:18.131428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T17:20:18.206954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
carting 8906
60.2%
ftl 5881
39.8%

Most occurring characters

ValueCountFrequency (%)
C 8906
11.1%
a 8906
11.1%
r 8906
11.1%
t 8906
11.1%
i 8906
11.1%
n 8906
11.1%
g 8906
11.1%
F 5881
7.4%
T 5881
7.4%
L 5881
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 8906
11.1%
a 8906
11.1%
r 8906
11.1%
t 8906
11.1%
i 8906
11.1%
n 8906
11.1%
g 8906
11.1%
F 5881
7.4%
T 5881
7.4%
L 5881
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 8906
11.1%
a 8906
11.1%
r 8906
11.1%
t 8906
11.1%
i 8906
11.1%
n 8906
11.1%
g 8906
11.1%
F 5881
7.4%
T 5881
7.4%
L 5881
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 8906
11.1%
a 8906
11.1%
r 8906
11.1%
t 8906
11.1%
i 8906
11.1%
n 8906
11.1%
g 8906
11.1%
F 5881
7.4%
T 5881
7.4%
L 5881
7.4%

start_scan_to_end_scan
Real number (ℝ)

High correlation 

Distinct2203
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.42903
Minimum23
Maximum7898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:18.305575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile71
Q1149
median279
Q3632
95-th percentile1892.1
Maximum7898
Range7875
Interquartile range (IQR)483

Descriptive statistics

Standard deviation658.25494
Coefficient of variation (CV)1.2433299
Kurtosis11.100934
Mean529.42903
Median Absolute Deviation (MAD)164
Skewness2.8953365
Sum7828667
Variance433299.56
MonotonicityNot monotonic
2025-04-24T17:20:18.477746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115 51
 
0.3%
148 51
 
0.3%
87 50
 
0.3%
128 49
 
0.3%
113 49
 
0.3%
170 48
 
0.3%
105 48
 
0.3%
99 48
 
0.3%
150 47
 
0.3%
135 47
 
0.3%
Other values (2193) 14299
96.7%
ValueCountFrequency (%)
23 1
 
< 0.1%
26 2
 
< 0.1%
27 3
 
< 0.1%
28 4
 
< 0.1%
29 2
 
< 0.1%
30 2
 
< 0.1%
31 8
0.1%
32 4
 
< 0.1%
33 8
0.1%
34 11
0.1%
ValueCountFrequency (%)
7898 1
< 0.1%
7458 1
< 0.1%
6495 1
< 0.1%
5864 1
< 0.1%
5807 1
< 0.1%
5688 1
< 0.1%
5686 1
< 0.1%
4846 1
< 0.1%
4699 1
< 0.1%
4616 1
< 0.1%

actual_distance_to_destination
Real number (ℝ)

High correlation 

Distinct14771
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0902
Minimum9.0024614
Maximum2186.5318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:18.627866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.0024614
5-th percentile11.097212
Q122.777099
median48.287894
Q3163.59126
95-th percentile829.17455
Maximum2186.5318
Range2177.5293
Interquartile range (IQR)140.81416

Descriptive statistics

Standard deviation305.50298
Coefficient of variation (CV)1.8617991
Kurtosis13.699595
Mean164.0902
Median Absolute Deviation (MAD)33.249556
Skewness3.5629313
Sum2426401.7
Variance93332.072
MonotonicityNot monotonic
2025-04-24T17:20:18.763167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.18625572 2
 
< 0.1%
32.17709214 2
 
< 0.1%
17.48464859 2
 
< 0.1%
26.58161002 2
 
< 0.1%
25.1427725 2
 
< 0.1%
195.5579057 2
 
< 0.1%
23.03404243 2
 
< 0.1%
27.95996726 2
 
< 0.1%
18.03636646 2
 
< 0.1%
25.24434706 2
 
< 0.1%
Other values (14761) 14767
99.9%
ValueCountFrequency (%)
9.002461442 1
< 0.1%
9.003577518 1
< 0.1%
9.004037718 1
< 0.1%
9.006254595 1
< 0.1%
9.006827116 1
< 0.1%
9.007520875 1
< 0.1%
9.007658103 1
< 0.1%
9.01049775 1
< 0.1%
9.016697542 1
< 0.1%
9.018870024 1
< 0.1%
ValueCountFrequency (%)
2186.531787 1
< 0.1%
2186.098218 1
< 0.1%
2185.663823 1
< 0.1%
2185.480257 1
< 0.1%
2185.379628 1
< 0.1%
2185.371321 1
< 0.1%
2184.055501 1
< 0.1%
2183.749474 1
< 0.1%
2183.481081 1
< 0.1%
2183.315555 1
< 0.1%

actual_time
Real number (ℝ)

High correlation 

Distinct1850
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.30601
Minimum9
Maximum6265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:18.903673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile32
Q167
median148
Q3367
95-th percentile1472.4
Maximum6265
Range6256
Interquartile range (IQR)300

Descriptive statistics

Standard deviation561.51794
Coefficient of variation (CV)1.5759429
Kurtosis13.836985
Mean356.30601
Median Absolute Deviation (MAD)99
Skewness3.3751783
Sum5268697
Variance315302.39
MonotonicityNot monotonic
2025-04-24T17:20:19.042845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 134
 
0.9%
50 130
 
0.9%
42 121
 
0.8%
48 115
 
0.8%
38 111
 
0.8%
54 107
 
0.7%
72 103
 
0.7%
58 101
 
0.7%
66 99
 
0.7%
36 94
 
0.6%
Other values (1840) 13672
92.5%
ValueCountFrequency (%)
9 2
 
< 0.1%
10 4
 
< 0.1%
11 6
 
< 0.1%
12 8
 
0.1%
13 16
0.1%
14 28
0.2%
15 14
0.1%
16 19
0.1%
17 18
0.1%
18 31
0.2%
ValueCountFrequency (%)
6265 1
< 0.1%
5804 1
< 0.1%
5465 1
< 0.1%
5067 1
< 0.1%
5064 1
< 0.1%
4687 1
< 0.1%
4532 1
< 0.1%
4267 1
< 0.1%
4263 1
< 0.1%
4154 1
< 0.1%

osrm_time
Real number (ℝ)

High correlation 

Distinct814
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.99094
Minimum6
Maximum2032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:19.173404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14
Q129
median60
Q3168
95-th percentile710
Maximum2032
Range2026
Interquartile range (IQR)139

Descriptive statistics

Standard deviation271.45949
Coefficient of variation (CV)1.6861787
Kurtosis13.248508
Mean160.99094
Median Absolute Deviation (MAD)40
Skewness3.455256
Sum2380573
Variance73690.257
MonotonicityNot monotonic
2025-04-24T17:20:19.332325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 268
 
1.8%
20 262
 
1.8%
29 261
 
1.8%
23 240
 
1.6%
32 238
 
1.6%
22 233
 
1.6%
17 223
 
1.5%
33 215
 
1.5%
16 206
 
1.4%
21 203
 
1.4%
Other values (804) 12438
84.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 19
 
0.1%
8 35
 
0.2%
9 39
 
0.3%
10 88
0.6%
11 118
0.8%
12 167
1.1%
13 136
0.9%
14 147
1.0%
15 199
1.3%
ValueCountFrequency (%)
2032 2
 
< 0.1%
2031 2
 
< 0.1%
2030 1
 
< 0.1%
1917 1
 
< 0.1%
1873 3
< 0.1%
1872 1
 
< 0.1%
1871 5
< 0.1%
1870 4
< 0.1%
1869 2
 
< 0.1%
1867 1
 
< 0.1%

osrm_distance
Real number (ℝ)

High correlation 

Distinct14702
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.88741
Minimum9.0729
Maximum2840.081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:19.489411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.0729
5-th percentile14.96663
Q130.7569
median65.3028
Q3206.6442
95-th percentile969.58967
Maximum2840.081
Range2831.0081
Interquartile range (IQR)175.8873

Descriptive statistics

Standard deviation370.56556
Coefficient of variation (CV)1.817501
Kurtosis13.823126
Mean203.88741
Median Absolute Deviation (MAD)45.2799
Skewness3.5536187
Sum3014883.2
Variance137318.84
MonotonicityNot monotonic
2025-04-24T17:20:19.626252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.7346 2
 
< 0.1%
13.4217 2
 
< 0.1%
17.8604 2
 
< 0.1%
34.7344 2
 
< 0.1%
20.0424 2
 
< 0.1%
16.346 2
 
< 0.1%
27.7116 2
 
< 0.1%
37.0811 2
 
< 0.1%
78.7434 2
 
< 0.1%
13.0535 2
 
< 0.1%
Other values (14692) 14767
99.9%
ValueCountFrequency (%)
9.0729 1
< 0.1%
9.1364 1
< 0.1%
9.2903 1
< 0.1%
9.3227 1
< 0.1%
9.3301 1
< 0.1%
9.3353 1
< 0.1%
9.3908 1
< 0.1%
9.3999 1
< 0.1%
9.4185 1
< 0.1%
9.6473 1
< 0.1%
ValueCountFrequency (%)
2840.081 1
< 0.1%
2839.6603 1
< 0.1%
2839.3582 1
< 0.1%
2838.8811 1
< 0.1%
2837.7608 1
< 0.1%
2670.2059 1
< 0.1%
2555.7542 1
< 0.1%
2555.6566 1
< 0.1%
2555.5269 1
< 0.1%
2555.4428 1
< 0.1%

segment_actual_time_sum
Real number (ℝ)

High correlation 

Distinct1885
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.05917
Minimum9
Maximum6230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:19.759511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile32
Q166
median147
Q3364
95-th percentile1458.2
Maximum6230
Range6221
Interquartile range (IQR)298

Descriptive statistics

Standard deviation556.36591
Coefficient of variation (CV)1.5758432
Kurtosis13.833819
Mean353.05917
Median Absolute Deviation (MAD)99
Skewness3.3720425
Sum5220686
Variance309543.03
MonotonicityNot monotonic
2025-04-24T17:20:19.899239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 121
 
0.8%
41 112
 
0.8%
60 107
 
0.7%
55 100
 
0.7%
35 100
 
0.7%
53 98
 
0.7%
49 98
 
0.7%
39 98
 
0.7%
45 95
 
0.6%
74 94
 
0.6%
Other values (1875) 13764
93.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
10 4
 
< 0.1%
11 6
 
< 0.1%
12 9
 
0.1%
13 20
0.1%
14 27
0.2%
15 14
0.1%
16 16
0.1%
17 27
0.2%
18 28
0.2%
ValueCountFrequency (%)
6230 1
< 0.1%
5768 1
< 0.1%
5427 1
< 0.1%
5032 1
< 0.1%
5028 1
< 0.1%
4642 1
< 0.1%
4504 1
< 0.1%
4222 1
< 0.1%
4214 1
< 0.1%
4129 1
< 0.1%

segment_osrm_time_sum
Real number (ℝ)

High correlation 

Distinct1240
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.5116
Minimum6
Maximum2564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:20.033445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q130
median65
Q3184
95-th percentile833.7
Maximum2564
Range2558
Interquartile range (IQR)154

Descriptive statistics

Standard deviation314.67928
Coefficient of variation (CV)1.7432635
Kurtosis14.570176
Mean180.5116
Median Absolute Deviation (MAD)45
Skewness3.6029152
Sum2669225
Variance99023.049
MonotonicityNot monotonic
2025-04-24T17:20:20.175343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 221
 
1.5%
20 213
 
1.4%
19 210
 
1.4%
18 209
 
1.4%
22 208
 
1.4%
23 204
 
1.4%
21 188
 
1.3%
28 184
 
1.2%
35 180
 
1.2%
30 180
 
1.2%
Other values (1230) 12790
86.5%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 22
 
0.1%
8 25
 
0.2%
9 61
 
0.4%
10 79
0.5%
11 115
0.8%
12 151
1.0%
13 131
0.9%
14 125
0.8%
15 175
1.2%
ValueCountFrequency (%)
2564 1
< 0.1%
2530 1
< 0.1%
2510 1
< 0.1%
2494 1
< 0.1%
2422 1
< 0.1%
2350 1
< 0.1%
2346 1
< 0.1%
2326 1
< 0.1%
2320 1
< 0.1%
2313 1
< 0.1%

segment_osrm_distance_sum
Real number (ℝ)

High correlation 

Distinct14718
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.70547
Minimum9.0729
Maximum3523.6324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:20.299330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.0729
5-th percentile15.36056
Q132.57885
median69.7842
Q3216.5606
95-th percentile1106.2946
Maximum3523.6324
Range3514.5595
Interquartile range (IQR)183.98175

Descriptive statistics

Standard deviation416.84628
Coefficient of variation (CV)1.871738
Kurtosis15.372075
Mean222.70547
Median Absolute Deviation (MAD)49.1334
Skewness3.7140165
Sum3293145.7
Variance173760.82
MonotonicityNot monotonic
2025-04-24T17:20:20.449252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1177 2
 
< 0.1%
51.5355 2
 
< 0.1%
45.6356 2
 
< 0.1%
32.9795 2
 
< 0.1%
27.5998 2
 
< 0.1%
14.95 2
 
< 0.1%
16.1861 2
 
< 0.1%
38.1914 2
 
< 0.1%
50.6246 2
 
< 0.1%
19.5174 2
 
< 0.1%
Other values (14708) 14767
99.9%
ValueCountFrequency (%)
9.0729 1
< 0.1%
9.1364 1
< 0.1%
9.2903 1
< 0.1%
9.3227 1
< 0.1%
9.3301 1
< 0.1%
9.3353 1
< 0.1%
9.3908 1
< 0.1%
9.3999 1
< 0.1%
9.4185 1
< 0.1%
9.6473 1
< 0.1%
ValueCountFrequency (%)
3523.6324 1
< 0.1%
3489.9866 1
< 0.1%
3456.0357 1
< 0.1%
3418.1829 1
< 0.1%
3367.3834 1
< 0.1%
3251.1052 1
< 0.1%
3250.869 1
< 0.1%
3238.744 1
< 0.1%
3213.4054 1
< 0.1%
3204.0404 1
< 0.1%

od_time_diff_hour
Real number (ℝ)

High correlation  Unique 

Distinct14787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.12445
Minimum23.461468
Maximum7898.552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:20.600771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.461468
5-th percentile71.203377
Q1150.93155
median287.42123
Q3670.64299
95-th percentile1939.7819
Maximum7898.552
Range7875.0905
Interquartile range (IQR)519.71144

Descriptive statistics

Standard deviation668.16505
Coefficient of variation (CV)1.2234666
Kurtosis10.25297
Mean546.12445
Median Absolute Deviation (MAD)172.71894
Skewness2.7775483
Sum8075542.2
Variance446444.53
MonotonicityNot monotonic
2025-04-24T17:20:20.764656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
354.4075714 1
 
< 0.1%
2260.1098 1
 
< 0.1%
181.6118738 1
 
< 0.1%
148.7947178 1
 
< 0.1%
158.3375369 1
 
< 0.1%
254.721952 1
 
< 0.1%
257.7081947 1
 
< 0.1%
113.8599436 1
 
< 0.1%
2182.120429 1
 
< 0.1%
1019.486964 1
 
< 0.1%
Other values (14777) 14777
99.9%
ValueCountFrequency (%)
23.46146848 1
< 0.1%
26.49916442 1
< 0.1%
26.5808515 1
< 0.1%
27.63686067 1
< 0.1%
27.73261922 1
< 0.1%
27.79866977 1
< 0.1%
28.17911532 1
< 0.1%
28.42774028 1
< 0.1%
28.92702358 1
< 0.1%
28.98354038 1
< 0.1%
ValueCountFrequency (%)
7898.551955 1
< 0.1%
7458.939519 1
< 0.1%
6496.022231 1
< 0.1%
5864.79313 1
< 0.1%
5807.995124 1
< 0.1%
5689.14172 1
< 0.1%
5686.743719 1
< 0.1%
4847.14009 1
< 0.1%
4699.692937 1
< 0.1%
4616.736291 1
< 0.1%

dest_state
Categorical

High correlation 

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
maharashtra
2549 
karnataka
2303 
haryana
1567 
tamil nadu
1043 
uttar pradesh
866 
Other values (27)
6459 

Length

Max length22
Median length16
Mean length9.273754
Min length3

Characters and Unicode

Total characters137131
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowuttar pradesh
2nd rowkarnataka
3rd rowharyana
4th rowmaharashtra
5th rowkarnataka

Common Values

ValueCountFrequency (%)
maharashtra 2549
17.2%
karnataka 2303
15.6%
haryana 1567
10.6%
tamil nadu 1043
 
7.1%
uttar pradesh 866
 
5.9%
gujarat 739
 
5.0%
telangana 730
 
4.9%
west bengal 665
 
4.5%
delhi 627
 
4.2%
rajasthan 556
 
3.8%
Other values (22) 3142
21.2%

Length

2025-04-24T17:20:20.925747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharashtra 2549
13.8%
karnataka 2303
12.5%
pradesh 1826
 
9.9%
haryana 1567
 
8.5%
tamil 1043
 
5.7%
nadu 1043
 
5.7%
uttar 866
 
4.7%
gujarat 739
 
4.0%
telangana 730
 
4.0%
west 665
 
3.6%
Other values (31) 5093
27.6%

Most occurring characters

ValueCountFrequency (%)
a 40243
29.3%
r 14837
 
10.8%
h 12011
 
8.8%
t 10670
 
7.8%
n 9084
 
6.6%
s 6474
 
4.7%
k 5242
 
3.8%
e 4834
 
3.5%
d 4826
 
3.5%
m 4388
 
3.2%
Other values (15) 24522
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 40243
29.3%
r 14837
 
10.8%
h 12011
 
8.8%
t 10670
 
7.8%
n 9084
 
6.6%
s 6474
 
4.7%
k 5242
 
3.8%
e 4834
 
3.5%
d 4826
 
3.5%
m 4388
 
3.2%
Other values (15) 24522
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 40243
29.3%
r 14837
 
10.8%
h 12011
 
8.8%
t 10670
 
7.8%
n 9084
 
6.6%
s 6474
 
4.7%
k 5242
 
3.8%
e 4834
 
3.5%
d 4826
 
3.5%
m 4388
 
3.2%
Other values (15) 24522
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 40243
29.3%
r 14837
 
10.8%
h 12011
 
8.8%
t 10670
 
7.8%
n 9084
 
6.6%
s 6474
 
4.7%
k 5242
 
3.8%
e 4834
 
3.5%
d 4826
 
3.5%
m 4388
 
3.2%
Other values (15) 24522
17.9%
Distinct919
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:21.284281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length17
Mean length7.4887401
Min length2

Characters and Unicode

Total characters110736
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)1.4%

Sample

1st rowkanpur
2nd rowdoddablpur
3rd rowgurgaon
4th rowmumbai
5th rowhospet
ValueCountFrequency (%)
mumbai 1159
 
7.4%
bengaluru 1122
 
7.2%
gurgaon 842
 
5.4%
delhi 510
 
3.3%
bangalore 503
 
3.2%
hyderabad 426
 
2.7%
chennai 413
 
2.7%
bhiwandi 396
 
2.5%
pune 296
 
1.9%
sonipat 278
 
1.8%
Other values (906) 9631
61.8%
2025-04-24T17:20:21.819349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20018
18.1%
r 8966
 
8.1%
n 8464
 
7.6%
u 8283
 
7.5%
i 6929
 
6.3%
h 6093
 
5.5%
b 5940
 
5.4%
g 5547
 
5.0%
d 5345
 
4.8%
l 4981
 
4.5%
Other values (17) 30170
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20018
18.1%
r 8966
 
8.1%
n 8464
 
7.6%
u 8283
 
7.5%
i 6929
 
6.3%
h 6093
 
5.5%
b 5940
 
5.4%
g 5547
 
5.0%
d 5345
 
4.8%
l 4981
 
4.5%
Other values (17) 30170
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20018
18.1%
r 8966
 
8.1%
n 8464
 
7.6%
u 8283
 
7.5%
i 6929
 
6.3%
h 6093
 
5.5%
b 5940
 
5.4%
g 5547
 
5.0%
d 5345
 
4.8%
l 4981
 
4.5%
Other values (17) 30170
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20018
18.1%
r 8966
 
8.1%
n 8464
 
7.6%
u 8283
 
7.5%
i 6929
 
6.3%
h 6093
 
5.5%
b 5940
 
5.4%
g 5547
 
5.0%
d 5345
 
4.8%
l 4981
 
4.5%
Other values (17) 30170
27.2%
Distinct914
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:22.167729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length8
Mean length7.4470143
Min length1

Characters and Unicode

Total characters110119
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)1.3%

Sample

1st rowcentral
2nd rowchikadpp
3rd rowbilaspur
4th rowmirard
5th rowhospet
ValueCountFrequency (%)
bilaspur 829
 
5.6%
central 767
 
5.1%
nelmngla 503
 
3.4%
mankoli 396
 
2.7%
bomsndra 358
 
2.4%
shamshbd 283
 
1.9%
kundli 276
 
1.9%
kgairprt 272
 
1.8%
mumbai 237
 
1.6%
tathawde 231
 
1.5%
Other values (907) 10754
72.1%
2025-04-24T17:20:22.678113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15478
14.1%
r 10739
 
9.8%
n 8757
 
8.0%
l 6465
 
5.9%
d 5956
 
5.4%
i 5955
 
5.4%
p 5703
 
5.2%
h 5497
 
5.0%
m 5123
 
4.7%
t 4755
 
4.3%
Other values (28) 35691
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15478
14.1%
r 10739
 
9.8%
n 8757
 
8.0%
l 6465
 
5.9%
d 5956
 
5.4%
i 5955
 
5.4%
p 5703
 
5.2%
h 5497
 
5.0%
m 5123
 
4.7%
t 4755
 
4.3%
Other values (28) 35691
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15478
14.1%
r 10739
 
9.8%
n 8757
 
8.0%
l 6465
 
5.9%
d 5956
 
5.4%
i 5955
 
5.4%
p 5703
 
5.2%
h 5497
 
5.0%
m 5123
 
4.7%
t 4755
 
4.3%
Other values (28) 35691
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15478
14.1%
r 10739
 
9.8%
n 8757
 
8.0%
l 6465
 
5.9%
d 5956
 
5.4%
i 5955
 
5.4%
p 5703
 
5.2%
h 5497
 
5.0%
m 5123
 
4.7%
t 4755
 
4.3%
Other values (28) 35691
32.4%
Distinct57
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:23.352467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length2
Mean length2.6654494
Min length2

Characters and Unicode

Total characters39414
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row6
2nd rowd
3rd rowhb
4th rowip
5th rownone
ValueCountFrequency (%)
d 3709
25.1%
hb 2087
14.1%
h 1651
11.2%
dc 1246
 
8.4%
i 976
 
6.6%
dpc 877
 
5.9%
none 801
 
5.4%
l 615
 
4.2%
ip 565
 
3.8%
hub 361
 
2.4%
Other values (49) 1915
12.9%
2025-04-24T17:20:23.644319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14002
35.5%
d 5856
14.9%
h 4144
 
10.5%
c 2550
 
6.5%
b 2461
 
6.2%
p 2075
 
5.3%
n 1691
 
4.3%
i 1646
 
4.2%
e 979
 
2.5%
o 853
 
2.2%
Other values (23) 3157
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14002
35.5%
d 5856
14.9%
h 4144
 
10.5%
c 2550
 
6.5%
b 2461
 
6.2%
p 2075
 
5.3%
n 1691
 
4.3%
i 1646
 
4.2%
e 979
 
2.5%
o 853
 
2.2%
Other values (23) 3157
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14002
35.5%
d 5856
14.9%
h 4144
 
10.5%
c 2550
 
6.5%
b 2461
 
6.2%
p 2075
 
5.3%
n 1691
 
4.3%
i 1646
 
4.2%
e 979
 
2.5%
o 853
 
2.2%
Other values (23) 3157
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14002
35.5%
d 5856
14.9%
h 4144
 
10.5%
c 2550
 
6.5%
b 2461
 
6.2%
p 2075
 
5.3%
n 1691
 
4.3%
i 1646
 
4.2%
e 979
 
2.5%
o 853
 
2.2%
Other values (23) 3157
 
8.0%

source_state
Categorical

High correlation 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
maharashtra
2682 
karnataka
2229 
haryana
1669 
tamil nadu
1085 
delhi
790 
Other values (24)
6332 

Length

Max length22
Median length16
Mean length9.0933252
Min length3

Characters and Unicode

Total characters134463
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmadhya pradesh
2nd rowkarnataka
3rd rowkarnataka
4th rowmaharashtra
5th rowkarnataka

Common Values

ValueCountFrequency (%)
maharashtra 2682
18.1%
karnataka 2229
15.1%
haryana 1669
11.3%
tamil nadu 1085
 
7.3%
delhi 790
 
5.3%
telangana 779
 
5.3%
gujarat 746
 
5.0%
uttar pradesh 719
 
4.9%
west bengal 677
 
4.6%
punjab 630
 
4.3%
Other values (19) 2781
18.8%

Length

2025-04-24T17:20:23.774621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maharashtra 2682
14.8%
karnataka 2229
12.3%
haryana 1669
 
9.2%
pradesh 1497
 
8.3%
tamil 1085
 
6.0%
nadu 1085
 
6.0%
delhi 790
 
4.4%
telangana 779
 
4.3%
gujarat 746
 
4.1%
uttar 719
 
4.0%
Other values (27) 4844
26.7%

Most occurring characters

ValueCountFrequency (%)
a 39853
29.6%
r 14322
 
10.7%
h 11686
 
8.7%
t 10443
 
7.8%
n 9126
 
6.8%
s 6169
 
4.6%
k 5038
 
3.7%
e 4736
 
3.5%
d 4496
 
3.3%
m 4466
 
3.3%
Other values (15) 24128
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134463
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 39853
29.6%
r 14322
 
10.7%
h 11686
 
8.7%
t 10443
 
7.8%
n 9126
 
6.8%
s 6169
 
4.6%
k 5038
 
3.7%
e 4736
 
3.5%
d 4496
 
3.3%
m 4466
 
3.3%
Other values (15) 24128
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134463
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 39853
29.6%
r 14322
 
10.7%
h 11686
 
8.7%
t 10443
 
7.8%
n 9126
 
6.8%
s 6169
 
4.6%
k 5038
 
3.7%
e 4736
 
3.5%
d 4496
 
3.3%
m 4466
 
3.3%
Other values (15) 24128
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134463
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 39853
29.6%
r 14322
 
10.7%
h 11686
 
8.7%
t 10443
 
7.8%
n 9126
 
6.8%
s 6169
 
4.6%
k 5038
 
3.7%
e 4736
 
3.5%
d 4496
 
3.3%
m 4466
 
3.3%
Other values (15) 24128
17.9%
Distinct679
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:24.181104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length17
Mean length7.4769054
Min length3

Characters and Unicode

Total characters110561
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)1.6%

Sample

1st rowbhopal
2nd rowtumkur
3rd rowbangalore
4th rowmumbai hub
5th rowbellary
ValueCountFrequency (%)
bengaluru 1015
 
6.6%
gurgaon 1013
 
6.6%
mumbai 893
 
5.8%
bhiwandi 811
 
5.3%
bangalore 755
 
4.9%
delhi 617
 
4.0%
hyderabad 562
 
3.6%
pune 445
 
2.9%
chandigarh 418
 
2.7%
kolkata 339
 
2.2%
Other values (668) 8576
55.5%
2025-04-24T17:20:24.922015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19689
17.8%
r 8818
 
8.0%
u 8127
 
7.4%
n 8096
 
7.3%
i 7138
 
6.5%
h 6379
 
5.8%
b 6317
 
5.7%
d 5923
 
5.4%
g 5685
 
5.1%
e 5447
 
4.9%
Other values (17) 28942
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 19689
17.8%
r 8818
 
8.0%
u 8127
 
7.4%
n 8096
 
7.3%
i 7138
 
6.5%
h 6379
 
5.8%
b 6317
 
5.7%
d 5923
 
5.4%
g 5685
 
5.1%
e 5447
 
4.9%
Other values (17) 28942
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 19689
17.8%
r 8818
 
8.0%
u 8127
 
7.4%
n 8096
 
7.3%
i 7138
 
6.5%
h 6379
 
5.8%
b 6317
 
5.7%
d 5923
 
5.4%
g 5685
 
5.1%
e 5447
 
4.9%
Other values (17) 28942
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 19689
17.8%
r 8818
 
8.0%
u 8127
 
7.4%
n 8096
 
7.3%
i 7138
 
6.5%
h 6379
 
5.8%
b 6317
 
5.7%
d 5923
 
5.4%
g 5685
 
5.1%
e 5447
 
4.9%
Other values (17) 28942
26.2%
Distinct713
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:25.408927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.397579
Min length3

Characters and Unicode

Total characters109388
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)1.5%

Sample

1st rowtrnsport
2nd rowveersagr
3rd rownelmngla
4th rowmumbai
5th rowbellary
ValueCountFrequency (%)
central 1039
 
7.0%
bilaspur 959
 
6.5%
mankoli 811
 
5.5%
nelmngla 732
 
4.9%
bomsndra 428
 
2.9%
mehmdpur 370
 
2.5%
east 363
 
2.4%
tathawde 361
 
2.4%
kgairprt 331
 
2.2%
mumbai 325
 
2.2%
Other values (707) 9141
61.5%
2025-04-24T17:20:26.143321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15560
14.2%
r 9555
 
8.7%
n 9070
 
8.3%
l 7525
 
6.9%
i 6422
 
5.9%
m 5797
 
5.3%
e 5756
 
5.3%
t 5269
 
4.8%
h 4979
 
4.6%
p 4769
 
4.4%
Other values (28) 34686
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15560
14.2%
r 9555
 
8.7%
n 9070
 
8.3%
l 7525
 
6.9%
i 6422
 
5.9%
m 5797
 
5.3%
e 5756
 
5.3%
t 5269
 
4.8%
h 4979
 
4.6%
p 4769
 
4.4%
Other values (28) 34686
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15560
14.2%
r 9555
 
8.7%
n 9070
 
8.3%
l 7525
 
6.9%
i 6422
 
5.9%
m 5797
 
5.3%
e 5756
 
5.3%
t 5269
 
4.8%
h 4979
 
4.6%
p 4769
 
4.4%
Other values (28) 34686
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15560
14.2%
r 9555
 
8.7%
n 9070
 
8.3%
l 7525
 
6.9%
i 6422
 
5.9%
m 5797
 
5.3%
e 5756
 
5.3%
t 5269
 
4.8%
h 4979
 
4.6%
p 4769
 
4.4%
Other values (28) 34686
31.7%
Distinct57
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:26.408136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length2
Mean length2.6577399
Min length2

Characters and Unicode

Total characters39300
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowh
2nd rowi
3rd rowh
4th rownone
5th rowdc
ValueCountFrequency (%)
hb 3305
22.3%
h 2739
18.5%
d 1519
10.2%
i 1329
9.0%
none 640
 
4.3%
ip 622
 
4.2%
1 597
 
4.0%
dpc 593
 
4.0%
dc 501
 
3.4%
hub 495
 
3.3%
Other values (51) 2498
16.8%
2025-04-24T17:20:26.818686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14198
36.1%
h 6584
16.8%
b 3821
 
9.7%
d 2693
 
6.9%
i 2043
 
5.2%
c 1916
 
4.9%
p 1897
 
4.8%
n 1336
 
3.4%
o 751
 
1.9%
1 694
 
1.8%
Other values (24) 3367
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14198
36.1%
h 6584
16.8%
b 3821
 
9.7%
d 2693
 
6.9%
i 2043
 
5.2%
c 1916
 
4.9%
p 1897
 
4.8%
n 1336
 
3.4%
o 751
 
1.9%
1 694
 
1.8%
Other values (24) 3367
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14198
36.1%
h 6584
16.8%
b 3821
 
9.7%
d 2693
 
6.9%
i 2043
 
5.2%
c 1916
 
4.9%
p 1897
 
4.8%
n 1336
 
3.4%
o 751
 
1.9%
1 694
 
1.8%
Other values (24) 3367
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14198
36.1%
h 6584
16.8%
b 3821
 
9.7%
d 2693
 
6.9%
i 2043
 
5.2%
c 1916
 
4.9%
p 1897
 
4.8%
n 1336
 
3.4%
o 751
 
1.9%
1 694
 
1.8%
Other values (24) 3367
 
8.6%

trip_month
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size115.7 KiB
9
13011 
10
1776 

Length

Max length2
Median length1
Mean length1.1201055
Min length1

Characters and Unicode

Total characters16563
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 13011
88.0%
10 1776
 
12.0%

Length

2025-04-24T17:20:26.985297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T17:20:27.093534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9 13011
88.0%
10 1776
 
12.0%

Most occurring characters

ValueCountFrequency (%)
9 13011
78.6%
1 1776
 
10.7%
0 1776
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 13011
78.6%
1 1776
 
10.7%
0 1776
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 13011
78.6%
1 1776
 
10.7%
0 1776
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 13011
78.6%
1 1776
 
10.7%
0 1776
 
10.7%

trip_hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.456212
Minimum0
Maximum23
Zeros991
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:27.212583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14
Q320
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)16

Descriptive statistics

Standard deviation7.9873869
Coefficient of variation (CV)0.64123726
Kurtosis-1.4960835
Mean12.456212
Median Absolute Deviation (MAD)7
Skewness-0.20609177
Sum184190
Variance63.79835
MonotonicityNot monotonic
2025-04-24T17:20:27.378072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
22 1123
 
7.6%
23 1107
 
7.5%
20 1080
 
7.3%
0 991
 
6.7%
21 872
 
5.9%
19 837
 
5.7%
1 748
 
5.1%
2 702
 
4.7%
18 696
 
4.7%
3 651
 
4.4%
Other values (14) 5980
40.4%
ValueCountFrequency (%)
0 991
6.7%
1 748
5.1%
2 702
4.7%
3 651
4.4%
4 635
4.3%
5 505
3.4%
6 610
4.1%
7 472
3.2%
8 345
 
2.3%
9 317
 
2.1%
ValueCountFrequency (%)
23 1107
7.5%
22 1123
7.6%
21 872
5.9%
20 1080
7.3%
19 837
5.7%
18 696
4.7%
17 595
4.0%
16 526
3.6%
15 469
3.2%
14 379
 
2.6%

trip_day
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.375127
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:27.515044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median19
Q325
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.8821985
Coefficient of variation (CV)0.42896022
Kurtosis-0.1620419
Mean18.375127
Median Absolute Deviation (MAD)5
Skewness-0.69524137
Sum271713
Variance62.129053
MonotonicityNot monotonic
2025-04-24T17:20:28.312371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
18 791
 
5.3%
15 783
 
5.3%
13 750
 
5.1%
12 747
 
5.1%
21 740
 
5.0%
22 740
 
5.0%
17 722
 
4.9%
14 712
 
4.8%
20 703
 
4.8%
25 695
 
4.7%
Other values (12) 7404
50.1%
ValueCountFrequency (%)
1 600
4.1%
2 549
3.7%
3 627
4.2%
12 747
5.1%
13 750
5.1%
14 712
4.8%
15 783
5.3%
16 616
4.2%
17 722
4.9%
18 791
5.3%
ValueCountFrequency (%)
30 506
3.4%
29 605
4.1%
28 605
4.1%
27 650
4.4%
26 683
4.6%
25 695
4.7%
24 658
4.4%
23 631
4.3%
22 740
5.0%
21 740
5.0%

trip_dayofweek
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9209441
Minimum0
Maximum6
Zeros1980
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size115.7 KiB
2025-04-24T17:20:28.575566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9276895
Coefficient of variation (CV)0.65995428
Kurtosis-1.1685046
Mean2.9209441
Median Absolute Deviation (MAD)2
Skewness0.065904383
Sum43192
Variance3.715987
MonotonicityNot monotonic
2025-04-24T17:20:28.665264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 2731
18.5%
5 2128
14.4%
3 2103
14.2%
4 2057
13.9%
1 2035
13.8%
0 1980
13.4%
6 1753
11.9%
ValueCountFrequency (%)
0 1980
13.4%
1 2035
13.8%
2 2731
18.5%
3 2103
14.2%
4 2057
13.9%
5 2128
14.4%
6 1753
11.9%
ValueCountFrequency (%)
6 1753
11.9%
5 2128
14.4%
4 2057
13.9%
3 2103
14.2%
2 2731
18.5%
1 2035
13.8%
0 1980
13.4%

Interactions

2025-04-24T17:20:13.854189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.050626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.663623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:57.536172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:59.766324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:01.486573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.393350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.864639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.284366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.317269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.801133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:11.730780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:14.046920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.182593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.788122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:57.721057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:59.958851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:01.646869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.523879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.023015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.416577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.441177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.967866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:11.926236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:14.183882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.310526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.896053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:57.884654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.089723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:01.760769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.657419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.133623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.543575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.555915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:10.134145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:12.106627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:14.760197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.441168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:55.018104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:58.055261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.237466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:02.293397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.786592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.248109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.667448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.679578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:10.298112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:12.280714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:14.874338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.572876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:55.147103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:58.228720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.386260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:02.415400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.919292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.360642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.814654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.826188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:10.464330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:12.449477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:14.992622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.708072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:55.250787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:58.391930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.538896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:02.539770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.038661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.475423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:07.432587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.945884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:10.615607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:12.605186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:15.108518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.841248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:55.372530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:58.554778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.673299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:02.674621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.153723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.595409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:07.558869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.069140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:10.775658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:12.775354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:15.235886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:53.987846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:55.488413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:58.731834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.806916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:02.791943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.269919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.722484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:07.678447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.185389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:10.940543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:12.953693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:15.354076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.135724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:56.868240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:58.917209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:00.952458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:02.920002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.389563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.836676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:07.817663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.308115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:11.110301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:13.145206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:15.471408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.273941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:57.041591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:59.186638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:01.104912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.046787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.507869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:05.959393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:07.951781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.432653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:11.268078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:13.346003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:15.575768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.402405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:57.198170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:59.372946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:01.228384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.164920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.629317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.057788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.071705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.549933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:11.408487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:13.512123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:15.692547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:54.532519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:57.362884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:19:59.589368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:01.355049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:03.275521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:04.746337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:06.172784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:08.194908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:09.684395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:11.566883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-24T17:20:13.686389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-24T17:20:28.770051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
actual_distance_to_destinationactual_timedest_stateod_time_diff_hourosrm_distanceosrm_timeroute_typesegment_actual_time_sumsegment_osrm_distance_sumsegment_osrm_time_sumsource_statestart_scan_to_end_scantrip_daytrip_dayofweektrip_hourtrip_month
actual_distance_to_destination1.0000.9030.1690.8400.9910.9790.5280.9020.9840.9680.1380.841-0.0040.0020.0850.033
actual_time0.9031.0000.1450.8970.9110.9060.4741.0000.9100.9020.1210.8980.001-0.0060.0240.020
dest_state0.1690.1451.0000.1570.1780.1910.4540.1420.1580.1620.7500.1520.0220.0000.1530.035
od_time_diff_hour0.8400.8970.1571.0000.8480.8430.5270.8970.8450.8370.1290.9950.0130.0050.0630.032
osrm_distance0.9910.9110.1780.8481.0000.9900.5180.9100.9910.9790.1410.849-0.0030.0020.0830.031
osrm_time0.9790.9060.1910.8430.9901.0000.5610.9040.9840.9900.1430.845-0.0040.0030.0860.029
route_type0.5280.4740.4540.5270.5180.5611.0000.4720.4760.5080.4040.5070.0430.0300.1520.027
segment_actual_time_sum0.9021.0000.1420.8970.9100.9040.4721.0000.9090.9010.1200.8980.001-0.0060.0240.020
segment_osrm_distance_sum0.9840.9100.1580.8450.9910.9840.4760.9091.0000.9890.1220.846-0.0030.0020.0810.032
segment_osrm_time_sum0.9680.9020.1620.8370.9790.9900.5080.9010.9891.0000.1270.839-0.0040.0030.0810.033
source_state0.1380.1210.7500.1290.1410.1430.4040.1200.1220.1271.0000.1270.0270.0000.1440.036
start_scan_to_end_scan0.8410.8980.1520.9950.8490.8450.5070.8980.8460.8390.1271.0000.0120.0050.0640.033
trip_day-0.0040.0010.0220.013-0.003-0.0040.0430.001-0.003-0.0040.0270.0121.0000.304-0.0091.000
trip_dayofweek0.002-0.0060.0000.0050.0020.0030.030-0.0060.0020.0030.0000.0050.3041.000-0.0280.408
trip_hour0.0850.0240.1530.0630.0830.0860.1520.0240.0810.0810.1440.064-0.009-0.0281.0000.021
trip_month0.0330.0200.0350.0320.0310.0290.0270.0200.0320.0330.0360.0331.0000.4080.0211.000

Missing values

2025-04-24T17:20:15.929816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-24T17:20:16.195270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trip_uuidtrip_creation_timeroute_schedule_uuidroute_typestart_scan_to_end_scanactual_distance_to_destinationactual_timeosrm_timeosrm_distancesegment_actual_time_sumsegment_osrm_time_sumsegment_osrm_distance_sumod_time_diff_hourdest_statedest_citydest_placedest_codesource_statesource_citysource_placesource_codetrip_monthtrip_hourtrip_daytrip_dayofweek
0trip-1536710416535487482018-09-12 00:00:16.535741thanos::sroute:d7c989ba-a29b-4a0b-b2f4-288cdc60074bFTL2259.0824.7328541562.0717.0991.35231548.01008.01320.47332260.109800uttar pradeshkanpurcentral6madhya pradeshbhopaltrnsporth90122
1trip-1536710422886051642018-09-12 00:00:22.886430thanos::sroute:3a1b0ab2-bb0b-4c53-8c59-eb2a2c0d68b9Carting180.073.186911143.068.085.1110141.065.084.1894181.611874karnatakadoddablpurchikadppdkarnatakatumkurveersagri90122
2trip-1536710433690995172018-09-12 00:00:33.691250thanos::sroute:de5e208e-7641-45e6-8100-4d9fb1e5720dFTL3933.01927.4042733347.01740.02354.06653308.01941.02545.26783934.362520haryanagurgaonbilaspurhbkarnatakabangalorenelmnglah90122
3trip-1536710460113304572018-09-12 00:01:00.113710thanos::sroute:f0176492-a679-4597-8332-bbd1c7f9f442Carting100.017.17527459.015.019.680059.016.019.8766100.494935maharashtramumbaimirardipmaharashtramumbai hubmumbainone90122
4trip-1536710529740466252018-09-12 00:02:09.740725thanos::sroute:d9f07b12-65e0-4f3b-bec8-df0613461b0fFTL717.0127.448500341.0117.0146.7918340.0115.0146.7919718.349042karnatakahospethospetnonekarnatakabellarybellarydc90122
5trip-1536710554161361662018-09-12 00:02:34.161600thanos::sroute:9bf03170-d0a2-4a3f-aa4d-9aaab3d98af4Carting189.024.59704861.023.028.064760.023.028.0647190.487849tamil naduchennaichennaipoonamalleetamil naduchennaiporurdpc90122
6trip-1536710662011381522018-09-12 00:04:22.011653thanos::sroute:a97698cc-846e-41a7-916b-88b17412610cCarting98.09.10051024.013.012.018424.013.012.018498.005634tamil naduchennaivandalurdctamil naduchennaichrompetdpc90122
7trip-1536710668263621652018-09-12 00:04:28.263977thanos::sroute:d5b71ae9-a11a-4f52-bcb7-274b65e2c513Carting146.022.42421064.034.028.920364.034.028.9203176.448324karnatakabengalurunwylhnkadckarnatakahbr layout pchbrnone90122
8trip-1536710740332849342018-09-12 00:05:40.333071thanos::sroute:a0e60427-16ad-4b17-b3b0-6a0664355ae1Carting280.025.454848161.029.030.9359161.029.030.9358310.804135gujaratsuratcentral3gujaratsuratcentral490122
9trip-1536710799565006912018-09-12 00:06:39.565253thanos::sroute:a10888ff-f794-41e1-9b7a-7f62ef663ddaCarting49.09.87214623.08.09.956623.014.016.086049.333390delhidelhidelhibhogaldelhidelhilajpatip90122
trip_uuidtrip_creation_timeroute_schedule_uuidroute_typestart_scan_to_end_scanactual_distance_to_destinationactual_timeosrm_timeosrm_distancesegment_actual_time_sumsegment_osrm_time_sumsegment_osrm_distance_sumod_time_diff_hourdest_statedest_citydest_placedest_codesource_statesource_citysource_placesource_codetrip_monthtrip_hourtrip_daytrip_dayofweek
14777trip-1538610894039733352018-10-03 23:54:54.039992thanos::sroute:233c5ce2-a1e2-4550-945c-28c357cafe57Carting98.033.89836149.027.036.442648.040.050.072598.726590gujaratanandvaghasiipgujaratvadodarakarelibaugdpc102332
14778trip-1538610895593021262018-10-03 23:54:55.593290thanos::sroute:9c1c7ad6-d0e4-4077-b574-4ab11de60bc1Carting186.028.11409186.030.034.922486.030.034.9223231.326709maharashtramumbaiulhasngrdcmaharashtramumbaieast21102332
14779trip-1538610898720284742018-10-03 23:54:58.720536thanos::sroute:27463ea7-5903-4530-92e7-6a4feca7b07cCarting181.027.01092662.028.038.286761.033.033.6400182.416663tamil naduchennaivepmpttudctamil naduchennaiporurdpc102332
14780trip-1538610901637681942018-10-03 23:55:01.637939thanos::sroute:9bf03170-d0a2-4a3f-aa4d-9aaab3d98af4Carting58.015.13500942.012.015.843641.011.015.843658.171907tamil naduchennaisriperumbudurdctamil naduchennaichennaipoonamallee102332
14781trip-1538610918430370402018-10-03 23:55:18.430664thanos::sroute:f0176492-a679-4597-8332-bbd1c7f9f442Carting88.017.76024838.016.020.506537.016.020.506588.215987maharashtramumbaimirardipmaharashtramumbai hubmumbainone102332
14782trip-1538610956258277842018-10-03 23:55:56.258533thanos::sroute:8a120994-f577-4491-9e4b-b7e4a1479d2fCarting257.057.76233283.062.073.463082.062.064.8551405.485842punjabzirakpurzirakpurdcpunjabchandigarhmehmdpurh102332
14783trip-1538611043862920512018-10-03 23:57:23.863155thanos::sroute:b30e1ec3-3bfa-4bd2-a7fb-3b757695fc1aCarting60.015.51378421.012.016.088221.011.016.088360.590521haryanafaridabadblbgarhdcharyanafbdbalabhgarhdpc102332
14784trip-1538611064429015552018-10-03 23:57:44.429324thanos::sroute:5609c268-e436-4e0a-8180-3db4a743161eCarting421.038.684839282.048.058.9037281.088.0104.8866422.119867uttar pradeshkanpurgovndngrdcuttar pradeshkanpurcentral6102332
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